bio-chipseq-differential-binding
Differential binding analysis using DiffBind. Compare ChIP-seq peaks between conditions with statistical rigor. Requires replicate samples. Outputs differentially bound regions with fold changes and p-values. Use when comparing ChIP-seq binding between conditions.
npx skills add majiayu000/claude-skill-registry --skill differential-binding-gptomics-bioskills-2 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Differential Binding with DiffBind ## Create Sample Sheet ```r # Create sample sheet as data frame or CSV samples <- data.frame( SampleID = c('ctrl_1', 'ctrl_2', 'treat_1', 'treat_2'), Tissue = c('cell', 'cell', 'cell', 'cell'), Factor = c('H3K4me3', 'H3K4me3', 'H3K4me3', 'H3K4me3'), Condition = c('control', 'control', 'treatment', 'treatment'), Replicate = c(1, 2, 1, 2), bamReads = c('ctrl1.bam', 'ctrl2.bam', 'treat1.bam', 'treat2.bam'), Peaks = c('ctrl1_peaks.narrowPeak', 'ctrl2_peaks.narrowPeak', 'treat1_peaks.narrowPeak', 'treat2_peaks.narrowPeak'), PeakCaller = c('macs', 'macs', 'macs', 'macs') ) write.csv(samples, 'samples.csv', row.names = FALSE) ``` ## Load Data ```r library(DiffBind) # From sample sheet dba_obj <- dba(sampleSheet = 'samples.csv') # View summary dba_obj ``` ## Count Reads in Peaks ```r # Count reads in consensus peaks (DiffBind 3.0+ defaults) # summits=250 and bUseSummarizeOverlaps=TRUE are now defaults dba_obj <- dba.count(dba_obj) # With specific parameters dba_obj <- dba.count( dba_obj, summits = 250, # Re-center peaks around summits (default in 3.0) minOverlap = 2 # Peak must be in at least 2 samples ) ``` ## Normalize Data ```r # Normalize (required
- Create Sample Sheet
- Load Data
- Count Reads in Peaks
- Normalize Data
- Set Up Contrast (DiffBind 3.0+)
- Run Differential Analysis
- View Results
- Filter Results
- Export Results
- Visualization
- Venn Diagram of Peaks
- Profile Plots
- Get Consensus Peaks
- Multi-Factor Design
What does the bio-chipseq-differential-binding skill do?
Differential binding analysis using DiffBind. Compare ChIP-seq peaks between conditions with statistical rigor. Requires replicate samples. Outputs differentially bound regions with fold changes and p-values. Use when comparing ChIP-seq binding between conditions.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill differential-binding-gptomics-bioskills-2 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.